{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import *\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "import tqdm\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "IMAGE_DIR = 'image_contest_level_2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>流=42072;圳=86;(圳-(97510*45921))*流/35864</td>\n",
       "      <td>-5.252849e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>回=38093;铁=50521;铁*(4560-64206-回/47726)</td>\n",
       "      <td>-3.013416e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>到=37808;(10220+到/78589)*(70612*88431)</td>\n",
       "      <td>6.381965e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>不=87863;42263*57806-不/76028*38980</td>\n",
       "      <td>2.443010e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>到=94310;锁=61045;((63526+锁)-21038)*到/81905</td>\n",
       "      <td>1.192137e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0             1\n",
       "0     流=42072;圳=86;(圳-(97510*45921))*流/35864 -5.252849e+09\n",
       "1     回=38093;铁=50521;铁*(4560-64206-回/47726) -3.013416e+09\n",
       "2      到=37808;(10220+到/78589)*(70612*88431)  6.381965e+13\n",
       "3          不=87863;42263*57806-不/76028*38980  2.443010e+09\n",
       "4  到=94310;锁=61045;((63526+锁)-21038)*到/81905  1.192137e+05"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('image_contest_level_2/labels.txt', sep=' ', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 统计字符串长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lens = np.array(map(lambda x:len(x.split(';')[1]), df[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lens.min(), lens.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 统计出现次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>250828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>250482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>250408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>250002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>249932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  word   count\n",
       "0    4  250828\n",
       "1    7  250482\n",
       "2    3  250408\n",
       "3    5  250002\n",
       "4    8  249932"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Counter(''.join([x.decode('utf-8') for x in df[0]]))\n",
    "d = pd.DataFrame(c.most_common(), columns=['word', 'count'])\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 7 3 5 8 9 6 1 2 0 ; = ) ( - / + * 不 锁 圳 深 烧 塘 柳 池 板 铁 烟 回 流 黄 复 上 之 君 来 奔 水 天 海 河 到 见\n"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b096afb90>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 536,
       "width": 963
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "sns.barplot(d['word'], d['count'], palette=\"Greens_d\")\n",
    "print ' '.join(d['word'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算出现频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 2.50828\n",
      "7 2.50482\n",
      "3 2.50408\n",
      "5 2.50002\n",
      "8 2.49932\n",
      "9 2.49755\n",
      "6 2.49637\n",
      "1 2.49601\n",
      "2 2.4955\n",
      "0 1.94757\n",
      "; 1.65807\n",
      "= 1.65807\n",
      ") 1.36505\n",
      "( 1.36505\n",
      "- 1.00065\n",
      "/ 1.0\n",
      "+ 0.9997\n",
      "* 0.99965\n",
      "不 0.23194\n",
      "锁 0.13332\n",
      "圳 0.13294\n",
      "深 0.1324\n",
      "烧 0.13206\n",
      "塘 0.13188\n",
      "柳 0.13156\n",
      "池 0.13094\n",
      "板 0.13094\n",
      "铁 0.1305\n",
      "烟 0.1296\n",
      "回 0.12274\n",
      "流 0.11972\n",
      "黄 0.11858\n",
      "复 0.11854\n",
      "上 0.1185\n",
      "之 0.11848\n",
      "君 0.1181\n",
      "来 0.11754\n",
      "奔 0.1175\n",
      "水 0.11718\n",
      "天 0.1171\n",
      "海 0.11662\n",
      "河 0.11624\n",
      "到 0.11586\n",
      "见 0.11536\n"
     ]
    }
   ],
   "source": [
    "n = len(df)\n",
    "for i in d.index:\n",
    "    print d['word'][i], d['count'][i] / float(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 猜括号生成方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1+1+1\n",
    "\n",
    "(1+1)+1+1\n",
    "1+(1+1)+1\n",
    "1+1+(1+1)\n",
    "(1+1+1)+1\n",
    "1+(1+1+1)\n",
    "\n",
    "((1+1)+1)+1\n",
    "(1+(1+1))+1\n",
    "\n",
    "1+((1+1)+1)\n",
    "1+(1+(1+1))\n",
    "\n",
    "(1+1)+(1+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3636363636363635"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*5/11.0+5/11.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画出运算符相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(</th>\n",
       "      <th>*</th>\n",
       "      <th>+</th>\n",
       "      <th>-</th>\n",
       "      <th>=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001547</td>\n",
       "      <td>-0.000878</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>0.004964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*</th>\n",
       "      <td>-0.001547</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.500745</td>\n",
       "      <td>-0.500644</td>\n",
       "      <td>-0.003324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+</th>\n",
       "      <td>-0.000878</td>\n",
       "      <td>-0.500745</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.498609</td>\n",
       "      <td>0.006420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-</th>\n",
       "      <td>0.002427</td>\n",
       "      <td>-0.500644</td>\n",
       "      <td>-0.498609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>=</th>\n",
       "      <td>0.004964</td>\n",
       "      <td>-0.003324</td>\n",
       "      <td>0.006420</td>\n",
       "      <td>-0.003092</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          (         *         +         -         =\n",
       "(  1.000000 -0.001547 -0.000878  0.002427  0.004964\n",
       "* -0.001547  1.000000 -0.500745 -0.500644 -0.003324\n",
       "+ -0.000878 -0.500745  1.000000 -0.498609  0.006420\n",
       "-  0.002427 -0.500644 -0.498609  1.000000 -0.003092\n",
       "=  0.004964 -0.003324  0.006420 -0.003092  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {}\n",
    "for c in '=+-*(':\n",
    "    data[c] = [x.count(c) for x in df[0]]\n",
    "\n",
    "df2 = pd.DataFrame(data)\n",
    "df2.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c9ce16d10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/b5FAGQDLsixjzBc4guVEqjlY1moYIiIiIuKtrc7X1h72t3K+ehrTXLIcT0H1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\no8sZJ2sbYJIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c9cfee510>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 357
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df2.corr(), annot=True, linewidths=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 猜测生成方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 1],\n",
       "       [0, 1, 1],\n",
       "       [0, 2, 0],\n",
       "       ..., \n",
       "       [2, 0, 2],\n",
       "       [0, 2, 2],\n",
       "       [1, 2, 0]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = np.random.randint(3, size=(10000, 3))\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.495757</td>\n",
       "      <td>-0.506633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.495757</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.497587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.506633</td>\n",
       "      <td>-0.497587</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  1.000000 -0.495757 -0.506633\n",
       "1 -0.495757  1.000000 -0.497587\n",
       "2 -0.506633 -0.497587  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {}\n",
    "for i in range(3):\n",
    "    data[i] = np.count_nonzero(test == i, axis=-1)\n",
    "\n",
    "df2 = pd.DataFrame(data)\n",
    "df2.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c9768b1d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lyIeSZMFkWZIkSSpgsixJkpSjZM9yDiyWJUmScmQbRhZsw5AkSZIKWCxLkiRlKDU31/xV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\ngf8Pfcw/DsTfM2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c97666710>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 357
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df2.corr(), annot=True, linewidths=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
